app.py

#Fast APi Packages
from fastapi import FastAPI
from pydantic import BaseModel
import json

#SkillExtraction Packages
from PyPDF2 import PdfReader
import psycopg2
from psycopg2 import sql
import pandas as pd
from datetime import date
import numpy as np
import spacy
import re
from sentence_transformers import SentenceTransformer, util
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from io import StringIO 
from spacy.matcher import PhraseMatcher
from skillNer.general_params import SKILL_DB
from skillNer.skill_extractor_class import SkillExtractor
from psycopg2.extensions import register_adapter, AsIs
register_adapter(np.int64, AsIs)
import warnings
warnings.filterwarnings('ignore')

#Custom Classes for endpoints
from DbConnection import DbConnection
from UploadFile import UploadOpenFile
from SkillExtract import SkillExtractor

import os
os.environ['TRANSFORMERS_CACHE'] = '/hug/cache/'

app = FastAPI()

class FileDetails(BaseModel):
    filecontents: str
    filename: str

class SkillDetails(BaseModel):
    skillid: int    
    
nlp = spacy.load("en_core_web_lg")
    # init skill extractor
skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher)

@app.get("/")
async def root():
 return {"greeting":"Hello world"}
#https://vaibhav84-resumeapi.hf.space/docs
db_params = DbConnection.GetDbConnection()
def parse_csv(df):
    res = df.to_json(orient="records")
    parsed = json.loads(res)
    return parsed
    
@app.get("/ProfileMatch") 
def ProfileMatchResults():
   dbQuery = "select * from profilematch"
   conn = psycopg2.connect(**db_params)   
   df = pd.read_sql_query(dbQuery, conn)
   return parse_csv(df) 

@app.post("/UploadFile/")
def UploadFileDetails(file_data: FileDetails):
   return UploadOpenFile.uploadFile(file_data.filecontents,file_data.filename,db_params)

@app.post("/ExtractSkills/")
def ExtractSkills(skill_data: SkillDetails):
   return SkillExtractor.SkillExtract(db_params,skill_extractor,skill_data.skillid)